The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Ponding water (also referred to as standing water, stagnated water, or pooled water) is typically an unwanted body of water that temporarily pools on a location, such as a field or roof, after rainfall. For example, after heavy rain, certain areas of a farmer's field based on geographical properties such as elevation and properties of the soil may experience a pooling of water. Although ponding water typically dissipates after some time, either through evaporation or absorption into the soil, in the meantime ponding water can be extremely detrimental to the growth of certain crops. For example, the ponding water may be pooled on top of the crops and cause the crops to become overwatered. Newly emerged seedlings, or crops such as turf grass that are short even at maturity, can become inundated and oxygen starved, resulting in severe damage or total loss under the ponded area. This is especially dangerous during early growing seasons when overwatering can cause a drastic decrease in crop yield come harvest.
In order to detect ponding water, farmers have typically had to physically traverse the fields after rainfall to determine if any ponding water has formed that could potentially harm the crops. If such dangerous accumulations of water are located, the farmers drain the area to keep the crops healthy and prevent losses in crop yield. However, modern farms can span extremely large areas and it may not be practical to physically visit all the potential areas where ponding water might appear. As a result, a field of research has grown around techniques to automatically detect ponding water from multispectral images captured by satellite. These spectral approaches typically rely on the fact that water strongly absorbs incoming radiation in the near to mid-infrared wavelengths. Thus, rather than the farmer physically going out to inspect the fields, satellite imagery of the agricultural fields can be taken on a periodic basis and analyzed to determine areas where standing water has accumulated.
Many standing water detection techniques have been developed in recent years. These techniques include simple thresholding using infrared bands, combinations of two or more bands or indices, such as the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), and the inclusion of auxiliary variables related to relief and topography for water detection purpose. Existing techniques tend to rely on large-scale water detection, such as permanent water or flooding events with Landsat images. However, such techniques translate poorly to detecting within-field ponding water.
There are many challenges to differentiating water signals from other land cover signals in remotely sensed data, such as satellite imagery data. Water quality and depth affect the spectral response particularly in the visible portion of the spectrum. In fact, a great deal of research has been devoted to techniques for monitoring water quality and bathymetry using remote sensing data. Other factors which create challenges for mapping standing water are related to the mixed spectral response of pixels covered partially by water (also called mixed pixels or “mixels”). Under certain resolutions, such as in 5 m resolution images, the pixels can be mixed of multiple end-members such as soil, residue, vegetation, water as in furrow irrigation or in flooded wetlands where vegetation is not completely submerged. Another issue comes from the fact that most high-resolution remote sensing imagery is not atmospherically corrected, so that the water content or aerosol depth impacts the sensed signals. Given these issues, within-field ponding water detection becomes an extremely challenging problem.